tf.contrib.training.batch_sequences_with_states(input_key, input_sequences, input_context, input_length, initial_states, num_unroll, batch_size, num_threads=3, capacity=1000, allow_small_batch=True, pad=True, name=None) Creates batches of segments of sequential input. This method creates a SequenceQueueingStateSaver (SQSS) and adds it to the queuerunners. It returns a NextQueuedSequenceBatch. It accepts one example at a time identified by a unique input_key. input_sequence is a dict with value

tf.contrib.learn.LinearClassifier.predict_proba(x=None, input_fn=None, batch_size=None, outputs=None, as_iterable=False) Runs inference to determine the class probability predictions.

tf.contrib.distributions.Chi2WithAbsDf.event_shape(name='event_shape') Shape of a single sample from a single batch as a 1-D int32 Tensor. Args: name: name to give to the op Returns: event_shape: Tensor.

tf.TensorArray.pack(name=None) Return the values in the TensorArray as a packed Tensor. All of the values must have been written and their shapes must all match. Args: name: A name for the operation (optional). Returns: All the tensors in the TensorArray packed into one tensor.

tf.contrib.distributions.Binomial.p Probability of success.

tf.contrib.learn.monitors.RunHookAdapterForMonitors.end(session)

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.std(name='std') Standard deviation.

tf.contrib.distributions.Chi2WithAbsDf.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.GammaWithSoftplusAlphaBeta.is_continuous

tf.contrib.bayesflow.stochastic_tensor.MultivariateNormalCholeskyTensor.clone(name=None, **dist_args)